Authors: Nico Schick, Franjo Čičak

Published on: February 04, 2024

Impact Score: 8.12

Arxiv code: Arxiv:2402.02598

Summary

• What is new: Introduction of a novel methodology to create a wide array of diverse and realistic safety-critical driving scenarios for testing.
• Why this is important: Lack of sufficient test data for evaluating intelligent driving functions in high-risk, safety-critical scenarios.
• What the research proposes: A new approach to generate diverse driving scenarios using kinematic equations and varying parameters, alongside the implementation of the Difference Space Stopping (DSS) metric for better safety evaluation.
• Results: Enhanced reliability and safety assessment for driver assistance and autonomous driving systems in challenging scenarios.

Technical Details

Technological frameworks used: nan

Models used: Kinematic equations, Difference Space Stopping (DSS) metric

Data used: Generated test data for safety-critical driving scenarios

Potential Impact

Automotive industry, specifically companies working on driver assistance systems and autonomous vehicles

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